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SAP HANA, Power Pivot, SQL Server – In-memory-Technologien im Vergleich

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SAP HANA, Power Pivot, SQL Server – In-memory-Technologien im Vergleich

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SAP HANA, Power Pivot, SQL Server – In-memory-Technologien im Vergleich

  1. 1. SAP HANA, Power Pivot, SQL Server – In-Memory-Technologien im Vergleich SQLSaturday Rheinland 201428.06.2014
  2. 2. Über mich – Marcel Franke Practice Lead Advanced Analytics & Data Science pmOne AG – Deutschland, Österreich, Schweiz P-TSP für Microsoft für APS und PDW Schwerpunkt: Data Warehouse, Big Data & Data Analytics Blog: dwjunkie.wordpress.com E-Mail: marcel.franke@pmOne.com
  3. 3. Organizer SQLSaturday Rheinland 201428.06.2014
  4. 4. You Rock! Sponsor SQLSaturday Rheinland 201428.06.2014
  5. 5. Gold Sponsor SQLSaturday Rheinland 201428.06.2014
  6. 6. Silver Sponsor SQLSaturday Rheinland 201428.06.2014
  7. 7. Bronze Sponsor and Media Partner SQLSaturday Rheinland 201428.06.2014
  8. 8. Agenda  Was passiert am Markt?  Warum ist In-Memory so beliebt?  In-Memory bei Microsoft  In-Memory bei SAP  Zusammenfassung und Vergleich
  9. 9. Was passiert am Markt? 28.06.2014 SQLSaturday Rheinland 2014
  10. 10. Alle haben In-Memory-Technologien 28.06.2014 SQLSaturday Rheinland 2014
  11. 11. In-Memory ist aber nichts Neues 28.06.2014 SQLSaturday Rheinland 2014 SAP HANA OLAP-Technologien
  12. 12. Ranking der Hersteller 28.06.2014 SQLSaturday Rheinland 2014 BI & Analytics Plattformen Data Warehouse
  13. 13. ABER WARUM IST IN-MEMORY SO HIP? 28.06.2014 SQLSaturday Rheinland 2014
  14. 14. Hintergrund
  15. 15. Quelle: Ray Kurzweil Quelle: Ray Kurzweil
  16. 16. Preisentwicklung Speicher 23.07.2014 © BARC 2013 16
  17. 17. Hintergrund – „Flaschenhälse verhindern“ Datenbank Applikation Calculation Calculation Zukünftiger Ansatz Klassischer Ansatz
  18. 18. Move data to compute or compute to data? 28.06.2014 SQLSaturday Rheinland 2014 move data to compute Datenbanken OLAP compute to data Daten
  19. 19. Technische Innovationen Hardware Innovationen 64bit address space – 2TB in current servers Dramatic decline in RAM price/performance Multi-Core Architecture Massive parallel (MPP) scaling with many nodes Row and Column Store Compression Partitioning No pre-calculated Aggregations Real-Time Data Capture Insert Only on Delta Software Innovationen
  20. 20. Warum In-Memory-Datenbanken? Steigende Daten Volumina Calculation Speed Art und Anzahl der Daten Quellen Geringe Transparenz Information nur auf hoher Aggregation verfügbar. Planungen und Analysen basieren oft auf veralteten Daten (Latenz: Tage, Wochen) Reaktives Business Model Verlorene Opportunities und Nachteile aufgrund mangelnder Agilität und Geschwindigkeit Geringe Reaktionsgeschwindigkeit Durch hohe Daten Latenz und Deployment Komplexität Derzeitige Situation Informations- Latenz
  21. 21. Warum In-Memory Computing? TeraBytes an Daten In-Memory Skalierbarer Daten Througput Real Time Hoch-Flexible Strukturen Business Performance verbessern  Lösungen können schnell und iterativ deployed werden  Planung und Simulation „on the fly“ auf nicht-aggregierten Daten Grundlage für Advanced und Predictive Analytics Flexibilität steigern  Iterative Entwicklungszyklen werden ermöglicht  Evolutionäre Vorgehensmodelle werden ermöglicht Zukünftige Situation
  22. 22. IN-MEMORY BEI MICROSOFT 28.06.2014 SQLSaturday Rheinland 2014
  23. 23. In-Memory bei Microsoft xVelocity Personal BI Team BI Corporate BI Power Pivot O365 Power BI Excel SQL Server 2014 Memory Optimized Tables
  24. 24. WIE FUNKTIONIEREN MEMORY OPTIMIZED TABLES? 28.06.2014 SQLSaturday Rheinland 2014
  25. 25. Anlegen einer Tabelle
  26. 26. Architektur 28.06.2014 SQLSaturday Rheinland 2014  adad
  27. 27. WIE FUNKTIONIERT DER CLUSTERED COLUMNSTORE? 28.06.2014 SQLSaturday Rheinland 2014
  28. 28. Zeilen werden zu Spalten Product Customer Date Sale Beer Thomas 2011-11-25 2 GBP Beer Thomas 2011-11-25 2 GBP Vodka Thomas 2011-11-25 10 GBP Whiskey Christian 2011-11-25 5 GBP Whiskey Christian 2011-11-25 5 GBP Vodka Alexei 2011-11-25 10 GBP Vodka Alexei 2011-11-25 10 GBP Sales ID Value 1 Beer 2 Beer 3 Vodka 4 Whiskey 5 Whiskey 6 Vodka 7 Vodka ID Customer 1 Thomas 2 Thomas 3 Thomas 4 Christian 5 Christian 6 Alexei 7 Alexei Product Customer Und so weiter… bis…
  29. 29. Und wir bekommen… ID Value 1 Beer 2 Beer 3 Vodka 4 Whiskey 5 Whiskey 6 Vodka 7 Vodka ID Customer 1 Thomas 2 Thomas 3 Thomas 4 Christian 5 Christian 6 Alexei 7 Alexei Product Customer ID Date 1 2011-11-25 2 2011-11-25 3 2011-11-25 4 2011-11-25 5 2011-11-25 6 2011-11-25 7 2011-11-25 Date ID Sale 1 2 GBP 2 2 GBP 3 10 GBP 4 5 GBP 5 5 GBP 6 10 GBP 7 10 GBP Sale
  30. 30. Und was jetzt? ID Value 1 Beer 2 Beer 3 Vodka 4 Whiskey 5 Whiskey 6 Vodka 7 Vodka Product Run length Encode Product’ ID Value 1-2 Beer 3 Vodka 4-5 Whiskey 6-7 Vodka
  31. 31. Daten komprimieren ID Value 1-2 Beer 3 Vodka 4-5 Whiskey 6-7 Vodka ID Customer 1-3 Thomas 4-5 Christian 6-7 Alexei Product’ Customer’ ID Date 1-7 2011-11-25 Date’ ID Sale 1-2 2 GBP 3 10 GBP 4-5 5 GBP 6-7 10 GBP Sale’
  32. 32. Was verwenden wir wann? Memory optimized Tables  Optimiert für OLTP workloads  Gut für kleine und viele Transaktionen  Nicht gut bei großen Scans  Keine Kompression  Keine Indexstrukturen  Schnelle Zwischenspeicher Clustered Columnstore  Data Warehouse Queries  Selektion einzelner Spalten  Gute Kompression der Daten 28.06.2014 SQLSaturday Rheinland 2014
  33. 33. Wie kompatibel ist In-Memory? 28.06.2014 SQLSaturday Rheinland 2014 xVelocity Power PivotSQL Server 2014
  34. 34. Laden der Daten nach Power Pivot  Test: 20 Mio. Zeilen große Tabelle  Daten werden unterschiedlich im SQL Server gehalten (CI, CCI, MOT)  Ergebnis  CI: 2m 47s  CCI: 2m 46s  MOT: 4m 20s 28.06.2014 SQLSaturday Rheinland 2014 Fazit: In-Memory ist nicht kompatibel
  35. 35. VERGLEICH DER KOMPRESSION 28.06.2014 SQLSaturday Rheinland 2014
  36. 36. Kompression 28.06.2014 SQLSaturday Rheinland 2014
  37. 37. Vergleich zwischen Herstellern 28.06.2014 SQLSaturday Rheinland 2014
  38. 38. IN-MEMORY IN SAP HANA 28.06.2014 SQLSaturday Rheinland 2014
  39. 39. In-Memory in SAP SAP HANA Personal BI Team BI Corporate BI HANA Information Composer SAP BO Lumira Excel SAP BW Workspace SAP BO LiveOffice HANA Studio
  40. 40. SAP HANA Ecosystem 28.06.2014 SQLSaturday Rheinland 2014
  41. 41. SAP HANA Platform 28.06.2014 SQLSaturday Rheinland 2014
  42. 42. SAP HANA Architektur 28.06.2014 SQLSaturday Rheinland 2014
  43. 43. In-Memory in HANA  Reine In-Memory Datenbank  OLTP + OLAP in der gleichen Datenbank  Derzeit: 80 Cores, 1 TB RAM in einem Server -> 5-6 TB Data Warehouse  Hauptspeicher kann pro Instanz verteilt werden  Workload Management: Auf der Roadmap  28.06.2014 SQLSaturday Rheinland 2014
  44. 44. Demo SAP HANA
  45. 45. Zusammenfassung 28.06.2014 SQLSaturday Rheinland 2014
  46. 46. Zusammenfassung: Microsoft und SAP • Beide Hersteller bieten hoch-performante in-Memory Technologien an • Beide Hersteller bieten In-Memory Technologien für OLAP & OLTP Workloads an. BI Users Data Discovery Data Storage & Operations Zentraler MetaDaten- Layer Engine läuft auf Server und Clients Eine oder mehrere zentrale HANA- Instanzen Zentralistische Architektur Verteilte Architektur
  47. 47. Fragen & Antworten
  48. 48. Thank you! for sponsorship for volunteering for participation for a great SQLSaturday #313 SQLSaturday Rheinland 201428.06.2014

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